Prediction of Glioblastoma Prediction of Glioblastoma Multiforme Patient Survival Using Multiforme Patient Survival Using

MR Image Features and Gene MR Image Features and Gene ExpressionExpression

Control/Tracking Number: Control/Tracking Number: 11-O-1519-11-O-1519-ASNRASNR

Nicolasjilwan, M.1Nicolasjilwan, M.1··Clifford, R.2Clifford, R.2··Raghavan, Raghavan, P.1P.1··Wintermark, M.1Wintermark, M.1··Hammoud, D.3Hammoud, D.3··Huang, E.4Huang, E.4··Jaffe, Jaffe,

C.5C.5··Freymann, J.2Freymann, J.2··Kirby, J.2Kirby, J.2··Buetow, K.4Buetow, K.4··Huang, Huang, S.6S.6··Holder, C.6Holder, C.6··Gutman, D.6Gutman, D.6··Flanders, A. E.7Flanders, A. E.7

1University of Virginia, Charlottesville, VA, 2SAIC-Frederick, Inc., Frederick, 1University of Virginia, Charlottesville, VA, 2SAIC-Frederick, Inc., Frederick, MD, 3National Institute of Health, Bethesda, MD, 4National Cancer MD, 3National Institute of Health, Bethesda, MD, 4National Cancer

Institute, Bethesda, MD, 5Boston University School of Medicine, Boston, Institute, Bethesda, MD, 5Boston University School of Medicine, Boston, MA, 6Emory University Hospital, Atlanta, GA, 7Thomas Jefferson MA, 6Emory University Hospital, Atlanta, GA, 7Thomas Jefferson

University Hospital, Philadelphia, PA.University Hospital, Philadelphia, PA.

DISCLOSUREDISCLOSURE

Nothing to discloseNothing to disclose

PURPOSEPURPOSE

Utilize conventional MRI imaging Utilize conventional MRI imaging features to predict survival of patients features to predict survival of patients with glioblastoma multiforme (GBM) with glioblastoma multiforme (GBM) after initial diagnosis.after initial diagnosis.

Linear regression models Linear regression models incorporating MR imaging features incorporating MR imaging features and tumor gene expression to predict and tumor gene expression to predict patient survival.patient survival.

Important role in selecting treatment Important role in selecting treatment options. options.

Materials & Methods

The study is part of The Cancer Genome The study is part of The Cancer Genome Atlas (TCGA) MR imaging (MRI) Atlas (TCGA) MR imaging (MRI) characterization project of the National characterization project of the National Cancer Institute. Cancer Institute.

MR images for 70 GBM patients made MR images for 70 GBM patients made available through the National Biomedical available through the National Biomedical Imaging Archive were reviewed Imaging Archive were reviewed independently by six neuroradiologists. independently by six neuroradiologists.

The VASARI feature scoring system for The VASARI feature scoring system for human gliomas, developed at Thomas human gliomas, developed at Thomas Jefferson University Hospital, was Jefferson University Hospital, was employed.employed.

30 features clustered by categories.30 features clustered by categories.– Lesion LocationLesion Location– Morphology of Lesion SubstanceMorphology of Lesion Substance– Morphology of Lesion MarginMorphology of Lesion Margin– Alterations in Vicinity of LesionAlterations in Vicinity of Lesion– Extent of resectionExtent of resection

620 genes associated with angiogenesis used in this investigation.

Survival was recoded as a binary Survival was recoded as a binary categorical variable: survival less than or categorical variable: survival less than or greater than 1 year. greater than 1 year.

Associations between imaging features and Associations between imaging features and survival were assessed using linear survival were assessed using linear regression models. Survival was the regression models. Survival was the outcome; imaging features were the outcome; imaging features were the predictors.predictors.

Well marginated Non-enhancingWell marginated Non-enhancing

F4 Enhancement Quality: 1=None 2=Mild/Minimal 3=Marked/Avid

F13 Definition of the non-enhancing margin 1= n/a 2= Smooth 3= Irregular Courtesy Dr Adam Flanders

Predominantly Non-Predominantly Non-enhancingenhancing

F5 Proportion Enhancing: 1= n/a 2=None (0%) 3= <5% 4= 6-33% 5= 34-67% 6= 68-95% 7= >95% 8=All (100%)

Courtesy Dr Adam Flanders

RESULTSRESULTS

Univariate Analysis of Association Univariate Analysis of Association between VASARI features and between VASARI features and

survivalsurvival

Individually, 6 MRI features show association to Individually, 6 MRI features show association to survival with an unadjusted p-value < 0.05.survival with an unadjusted p-value < 0.05.

Negative correlation with survival:Negative correlation with survival: Ependymal extension (F19), (P = 0.0012)Ependymal extension (F19), (P = 0.0012) Longest dimension of lesion size (F29)Longest dimension of lesion size (F29) Deep white matter invasion (F21) Deep white matter invasion (F21) The presence of satellites (F24).The presence of satellites (F24).

Positive correlation with survival: Positive correlation with survival: Location of the tumor in the right (usually non-Location of the tumor in the right (usually non-

dominant) hemisphere (F2).dominant) hemisphere (F2). Frontal lobe location (feature F1a), (P = Frontal lobe location (feature F1a), (P =

0.0098).0.0098).

Linear regression models incorporating the Linear regression models incorporating the most significant VASARI feature, F19 most significant VASARI feature, F19

(ependymal extension), and expression of (ependymal extension), and expression of angiogenesis-related genes.angiogenesis-related genes.

4 genes individually improve the predictive power 4 genes individually improve the predictive power of F19 (ependymal extension). of F19 (ependymal extension).

Expression of Expression of ANG (angiogenin)ANG (angiogenin) and and TGFB2 TGFB2 (TGF-beta 2)(TGF-beta 2) genes genes negatively correlates with negatively correlates with survivalsurvival..

CCL5 (chemokine (C-C motif) ligand 5)CCL5 (chemokine (C-C motif) ligand 5) and and TNF (tumor necrosis factor)TNF (tumor necrosis factor) positively positively correlate with survivalcorrelate with survival..

Feature F19 correctly predicts survival for Feature F19 correctly predicts survival for 72%72% of of the cases.the cases. A model based on ependymal A model based on ependymal extension, CCL5, ANG, TGFB2 and TNF correctly extension, CCL5, ANG, TGFB2 and TNF correctly predicts survival for predicts survival for 82%82% of patients. of patients.

CONCLUSIONCONCLUSION

A subset of VASARI imaging features correlate well A subset of VASARI imaging features correlate well with patient survival. with patient survival.

Linear regression models incorporating multiple Linear regression models incorporating multiple imaging features or a single VASARI feature imaging features or a single VASARI feature (ependymal extension) and tumor gene expression (ependymal extension) and tumor gene expression can be used to predict patient survival. can be used to predict patient survival.

We are refining these models and are investigating We are refining these models and are investigating whether including patient clinical characteristics whether including patient clinical characteristics into linear models can improve their predictive into linear models can improve their predictive power.power.

AKNOWLEDGMENTAKNOWLEDGMENT

University of VirginiaUniversity of VirginiaSAIC-FrederickSAIC-Frederick

National Institute of HealthNational Institute of Health

National Cancer InstituteNational Cancer Institute

Thomas Jefferson University Hospital Thomas Jefferson University Hospital

Emory University HospitalEmory University Hospital

Boston University School of MedicineBoston University School of Medicine